Designing Autonomous AI: A Guide for Machine Teaching
- Length: 250 pages
- Edition: 1
- Language: English
- Publisher: O'Reilly Media
- Publication Date: 2022-07-19
- ISBN-10: 1098110757
- ISBN-13: 9781098110758
- Sales Rank: #1491350 (See Top 100 Books)
Early rules-based artificial intelligence demonstrated intriguing decision-making capabilities but lacked perception and didn’t learn. AI today, primed with machine learning perception and deep reinforcement learning capabilities, can perform superhuman decision-making for specific tasks. This book shows you how to combine the practicality of early AI with deep learning capabilities and industrial control technologies to make robust decisions in the real world.
Using concrete examples, minimal theory, and a proven architectural framework, author Kence Anderson demonstrates how to teach autonomous AI explicit skills and strategies. You’ll learn when and how to use and combine various AI architecture design patterns, as well as how to design advanced AI without needing to manipulate neural networks or machine learning algorithms. Students, process operators, data scientists, machine learning algorithm experts, and engineers who own and manage industrial processes can use the methodology in this book to design autonomous AI.
This book examines:
- Differences between and limitations of automated, autonomous, and human decision-making
- Unique advantages of autonomous AI for real-time decision-making, with use cases
- How to design an autonomous AI from modular components and document your designs
Preface What Is Autonomous AI? Who should read this book? Process Experts Data Scientists and Software Engineers Innovation Leaders Teachers Problem Solvers What can you expect to learn from this book? Introduction: The Right Brain in the Right Place—Why do we need Autonomous AI? The changing world requires adapting skills Problems need solutions, not AI What can AI do for me in real life? AI Decision Making is becoming more Autonomous Beware of Data Science Colonialism The changing workforce demands transferred skills Expertise is hard to aquire Expertise is hard to maintain Expertise is simple to teach, but requires practice Pressing problems demand completely new skills AI is a tool; use it for good I. When automation doesn’t work 1. Sometimes machines make bad decisions Math, Menus, and Manuals: How Machines Make Automated Decisions Control theory uses math to calculate decisions Optimization algorithms use menus of options to evaluate decisions Expert Systems recall stored expertise, like manuals 2. The Quest for More Human-Like Decision Making Augmenting Human Intelligence How humans make decisions and acquire skills Humans act on what they perceive Humans build complex correlations into their intuition with practice Humans abstract to strategy for complex tasks There’s a new kind of AI in town Reinforcement Learning learns what to do by practicing Neural Networks can correlate any relationship between variables When should I use Deep Reinforcement Learning? Limitations of Deep Reinforcement Learning The Superpowers of Autonomous AI Autonomous AI makes more human-like decisions Autonomous AI perceives, then acts The difference between perception and action in AI Autonomous AI learns and adapts when things change Autonomous AI can spot patterns Autonomous AI infers from experience Autonomous AI improvises and strategizes Autonomous AI can plan for the long-term future Autonomous AI brings together the best of all decision-making technologies When should you use Autonomous AI? When the superpowers matter most When humans need to take over the decision making process Autonomous AI is like a brilliant, curious toddler that needs to be taught II. What Is Machine Teaching? 3. How Brains Learn Best Learning multiple skills simultaneously is hard for humans and AI Teaching Skills and Strategies explicitly Teaching allows us to trust AI The Mindset of a Machine Teacher Teacher more than programmer Learner more than expert What is a brain design? How decision-making works Acquiring skill is like exploring an unknown area A brain design is a mental map that guides exploration with landmarks 4. Building Blocks for Machine Teaching Case Study: Learning to walk is hard to evolve, easier to teach So, why do we walk? Strategy vs. Evolution Teaching walking as three skills Brains are built from skills What is a skill? Perception concepts discern or recognize Action concepts decide and act Selector concepts supervise and assign Brains are organized by functions and strategies Sequences or parallel execution for functional skills Hierarchies for strategies Visual Language of Brain Design III. How Do You Teach a Machine? Understanding the process Meet with experts Ask the right questions Case study: Let’s design a smart thermostat together 5. Teaching Your AI Brain What to Do Determining which actions the brain will take Perception is required, but not enough Sequential decisions Triggering the action in your AI brain Setting the decision frequency Handling delayed consequences for brain actions Actions for Smart Thermostat 6. Setting Goals for Your AI Brain There’s always a tradeoff Throughput versus Efficiency Supervisors have different goals than crews do Don’t prioritize goals, balance them Watch out for expert rules disguised as goals Ideal versus Available Setting Goals Step 1: Identify Scenarios Step 2: Match goals to scenarios Step 3: Teach strategies for each scenario Types of Goals Maximize, like profit Minimize, lirrors Reach, like the finish line for a race Maintain, like the temperature for a thermostat Avoid, like dangerous conditions Standardize, like the heat in an oven Smooth, like a line Expanding task algebra to include goals Setting goals for Smart Thermostat About the Author
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